71 citations · 170 across the 38 of their papers we have counts for
39 papers
Alignment of Similarity-Transformed Images Based on Fourier--Mellin Transform Using Auxiliary Function Method
Shinji Yamashita, Yuma Kinoshita, Hitoshi Kiya
This paper proposes an algorithm for estimating the similarity transformation, namely translation, scale, and rotation, between two images with subpixel accuracy. Image registratio…
Boundary-Continuous Cross-Camera RGB Mapping via Hue-Split Model Trees
Yuma Kinoshita, Hitoshi Kiya
We propose a hue-split model-tree method for boundary-continuous cross-camera RGB mapping. Cross-camera RGB mapping aims to produce consistent color representations across cameras…
Scale and Rotation Estimation of Similarity-Transformed Images via Cross-Correlation Maximization Based on Auxiliary Function Method
Shinji Yamashita, Yuma Kinoshita, Hitoshi Kiya
This paper introduces a highly efficient algorithm capable of jointly estimating scale and rotation between two images with sub-pixel precision. Image alignment serves as a critica…
Pre-training Autoencoder for Acoustic Event Classification via Blinky
Xiaoyang Liu, Yuma Kinoshita
In the acoustic event classification (AEC) framework that employs Blinkies, audio signals are converted into LED light emissions and subsequently captured by a single video camera.…
Scene-Segmentation-Based Exposure Compensation for Tone Mapping of High Dynamic Range Scenes
Yuma Kinoshita, Hitoshi Kiya
We propose a novel scene-segmentation-based exposure compensation method for multi-exposure image fusion (MEF) based tone mapping. The aim of MEF-based tone mapping is to display h…
Template matching with white balance adjustment under multiple illuminants
Teruaki Akazawa, Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel template matching method with a white balancing adjustment, called N-white balancing, which was proposed for multi-illuminant scenes. To reduce th…
Image and Model Transformation with Secret Key for Vision Transformer
Hitoshi Kiya, Ryota Iijima, MaungMaung Aprilpyone +1
In this paper, we propose a combined use of transformed images and vision transformer (ViT) models transformed with a secret key. We show for the first time that models trained wit…
Privacy-Preserving Image Classification Using Vision Transformer
Zheng Qi, AprilPyone MaungMaung, Yuma Kinoshita +1
In this paper, we propose a privacy-preserving image classification method that is based on the combined use of encrypted images and the vision transformer (ViT). The proposed meth…
An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications
Hitoshi Kiya, AprilPyone MaungMaung, Yuma Kinoshita +2
This article presents an overview of image transformation with a secret key and its applications. Image transformation with a secret key enables us not only to protect visual infor…
Self-Supervised Intrinsic Image Decomposition Network Considering Reflectance Consistency
Yuma Kinoshita, Hitoshi Kiya
We propose a novel intrinsic image decomposition network considering reflectance consistency. Intrinsic image decomposition aims to decompose an image into illumination-invariant a…
Spatially varying white balancing for mixed and non-uniform illuminants
Teruaki Akazawa, Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel white balance adjustment, called "spatially varying white balancing," for single, mixed, and non-uniform illuminants. By using n diagonal matrices…
Separated-Spectral-Distribution Estimation Based on Bayesian Inference with Single RGB Camera
Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel method for separately estimating spectral distributions from images captured by a typical RGB camera. The proposed method allows us to separately…
Multi-color balance for color constancy
Teruaki Akazawa, Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel multi-color balance adjustment for color constancy. The proposed method, called "n-color balancing," allows us not only to perfectly correct n tar…
Deep Retinex Network for Estimating Illumination Colors with Self-Supervised Learning
Kouki Seo, Yuma Kinoshita, Hitoshi Kiya
We propose a novel Retinex image-decomposition network that can be trained in a self-supervised manner. The Retinex image-decomposition aims to decompose an image into illumination…
Multi-color balancing for correctly adjusting the intensity of target colors
Teruaki Akazawa, Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel multi-color balance method for reducing color distortions caused by lighting effects. The proposed method allows us to adjust three target-colors…
CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection
Takayuki Osakabe, Miki Tanaka, Yuma Kinoshita +1
In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection. Recent rapid advances in image manipulation tools and deep…
Checkerboard-Artifact-Free Image-Enhancement Network Considering Local and Global Features
Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel convolutional neural network (CNN) that never causes checkerboard artifacts, for image enhancement. In research fields of image-to-image translati…
Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluation
Hiroki Ito, Yuma Kinoshita, Hitoshi Kiya
We propose a transformation network for generating visually-protected images for privacy-preserving DNNs. The proposed transformation network is trained by using a plain image data…
Hue-Correction Scheme Considering Non-Linear Camera Response for Multi-Exposure Image Fusion
Kouki Seo, Chihiro Go, Yuma Kinoshita +1
We propose a novel hue-correction scheme for multi-exposure image fusion (MEF). Various MEF methods have so far been studied to generate higher-quality images. However, there are f…
A Privacy-Preserving Machine Learning Scheme Using EtC Images
Ayana Kawamura, Yuma Kinoshita, Takayuki Nakachi +2
We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption metho…
Hue Correction Scheme for Multi-Exposure Image Fusion Considering Hue Distortion in Input Images
Kouki Seo, Chihiro Go, Yuma Kinoshita +1
We propose a novel hue-correction scheme for multi-exposure image fusion (MEF). Various MEF methods have so far been studied to generate higher-quality images. However, there are f…
Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs
Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a fixed convolutional layer with an order of smoothness not only for avoiding checkerboard artifacts in convolutional neural networks (CNNs) but also for…
On the Security of Pixel-Based Image Encryption for Privacy-Preserving Deep Neural Networks
Warit Sirichotedumrong, Yuma Kinoshita, Hitoshi Kiya
This paper aims to evaluate the safety of a pixel-based image encryption method, which has been proposed to apply images with no visual information to deep neural networks (DNN), i…
Privacy-Preserving Machine Learning Using EtC Images
Ayana Kawamura, Yuma Kinoshita, Hitoshi Kiya
In this paper, we propose a novel privacy-preserving machine learning scheme with encrypted images, called EtC (Encryption-then-Compression) images. Using machine learning algorith…